Protein Interaction Interface Research Articles

DDMut-PPI: predicting effects of mutations on protein-protein interactions using graph-based deep learning.

Protein-protein interactions (PPIs) play a vital role in cellular functions and are essential for therapeutic development and understanding diseases. However, current predictive tools often struggle to balance efficiency and precision in predicting the effects of mutations on these complex interactions. To address this, we present DDMut-PPI, a deep learning model that efficiently and accurately predicts changes in PPI binding free energy upon single and multiple point mutations. Building on the robust Siamese network architecture with graph-based signatures from our prior work, DDMut, the DDMut-PPI model was enhanced with a graph convolutional network operated on the protein interaction interface. We used residue-specific embeddings from ProtT5 protein language model as node features, and a variety of molecular interactions as edge features. By integrating evolutionary context with spatial information, this framework enables DDMut-PPI to achieve a robust Pearson correlation of up to 0.75 (root mean squared error: 1.33 kcal/mol) in our evaluations, outperforming most existing methods. Importantly, the model demonstrated consistent performance across mutations that increase or decrease binding affinity. DDMut-PPI offers a significant advancement in the field and will serve as a valuable tool for researchers probing the complexities of protein interactions. DDMut-PPI is freely available as a web server and an application programming interface at https://biosig.lab.uq.edu.au/ddmut_ppi.

A chemical proteomics approach for global mapping of functional lysines on cell surface of living cell.

Cell surface proteins are responsible for many crucial physiological roles, and they are also the major category of drug targets as the majority of therapeutics target membrane proteins on the surface of cells to alter cellular signaling. Despite its great significance, ligand discovery against membrane proteins has posed a great challenge mainly due to the special property of their natural habitat. Here, we design a new chemical proteomic probe OPA-S-S-alkyne that can efficiently and selectively target the lysines exposed on the cell surface and develop a chemical proteomics strategy for global analysis of surface functionality (GASF) in living cells. In total, we quantified 2639 cell surface lysines in Hela cell and several hundred residues with high reactivity were discovered, which represents the largest dataset of surface functional lysine sites to date. We discovered and validated that hyper-reactive lysine residues K382 on tyrosine kinase-like orphan receptor 2 (ROR2) and K285 on Endoglin (ENG/CD105) are at the protein interaction interface in co-crystal structures of protein complexes, emphasizing the broad potential functional consequences of cell surface lysines and GASF strategy is highly desirable for discovering new active and ligandable sites that can be functionally interrogated for drug discovery.

Structural and functional validation of a highly specific Smurf2 inhibitor.

Smurf1 and Smurf2 are two closely related member of the HECT (homologous to E6AP carboxy terminus) E3 ubiquitin ligase family and play important roles in the regulation of various cellular processes. Both were initially identified to regulate transforming growth factor-β and bone morphogenetic protein signaling pathways through regulating Smad protein stability and are now implicated in various pathological processes. Generally, E3 ligases, of which over 800 exist in humans, are ideal targets for inhibition as they determine substrate specificity; however, there are few inhibitors with the ability to precisely target a particular E3 ligase of interest. In this work, we explored a panel of ubiquitin variants (UbVs) that were previously identified to bind Smurf1 or Smurf2. In vitro binding and ubiquitination assays identified a highly specific Smurf2 inhibitor, UbV S2.4, which was able to inhibit ligase activity with high potency in the low nanomolar range. Orthologous cellular assays further demonstrated high specificity of UbV S2.4 toward Smurf2 and no cross-reactivity toward Smurf1. Structural analysis of UbV S2.4 in complex with Smurf2 revealed its mechanism of inhibition was through targeting the E2 binding site. In summary, we investigated several protein-based inhibitors of Smurf1 and Smurf2 and identified a highly specific Smurf2 inhibitor that disrupts the E2-E3 protein interaction interface.

Scaffold Matcher: A CMA-ES based algorithm for identifying hotspot aligned peptidomimetic scaffolds.

The design of protein interaction inhibitors is a promising approach to address aberrant protein interactions that cause disease. One strategy in designing inhibitors is to use peptidomimetic scaffolds that mimic the natural interaction interface. A central challenge in using peptidomimetics as protein interaction inhibitors, however, is determining how best the molecular scaffold aligns to the residues of the interface it is attempting to mimic. Here we present the Scaffold Matcher algorithm that aligns a given molecular scaffold onto hotspot residues from a protein interaction interface. To optimize the degrees of freedom of the molecular scaffold we implement the covariance matrix adaptation evolution strategy (CMA-ES), a state-of-the-art derivative-free optimization algorithm in Rosetta. To evaluate the performance of the CMA-ES, we used 26 peptides from the FlexPepDock Benchmark and compared with three other algorithms in Rosetta, specifically, Rosetta's default minimizer, a Monte Carlo protocol of small backbone perturbations, and a Genetic algorithm. We test the algorithms' performance on their ability to align a molecular scaffold to a series of hotspot residues (i.e., constraints) along native peptides. Of the 4 methods, CMA-ES was able to find the lowest energy conformation for all 26 benchmark peptides. Additionally, as a proof of concept, we apply the Scaffold Match algorithm with CMA-ES to align a peptidomimetic oligooxopiperazine scaffold to the hotspot residues of the substrate of the main protease of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Our implementation of CMA-ES into Rosetta allows for an alternative optimization method to be used on macromolecular modeling problems with rough energy landscapes. Finally, our Scaffold Matcher algorithm allows for the identification of initial conformations of interaction inhibitors that can be further designed and optimized as high-affinity reagents.

Determination of Protein Monoclonal-Antibody Epitopes by a Combination of Structural Proteomics Methods.

Structural proteomics techniques are useful for the determination of protein interaction interfaces. Each technique provides orthogonal structural information on the structure and the location of protein interaction sites. Here, we have characterized a monoclonal antibody epitope for a protein antigen by a combination of differential photoreactive surface modification (SM), cross-linking (CL), differential hydrogen-deuterium exchange (HDX), and epitope extraction/excision. We found that experimental data from different approaches agree with each other in determining the epitope of the monoclonal antibody on the protein antigens using the HIV-1 p24-mAb E complex as an illustrative example. A combination of these multiple structural proteomics approaches results in a detailed picture of the interaction of the proteins and increases confidence in the determination of the final structure of the protein interaction interface. Data are available via ProteomeXchange with identifier PXD040902.

Backbone assignment and inhibitor binding studies of IL-33 mutants by NMR spectroscopy

Interleukin-33 (IL-33) is an IL-1 family protein that induces a type-2 immune response. IL-33 is constitutively expressed in epithelial cells and released in response to the cell damage or stimulation by an allergen. The secreted protein is activated when the N-terminal domain is cleaved by a protease, and the active form signals downstream immune cells, such as eosinophils, by binding to the heterodimeric ST2:IL-1RAcP receptor complex on the cell surface. The binding stimulates an inflammatory response, and the abnormal inflammatory response can cause allergic diseases such as atopic dermatitis and asthma. Inhibition of the interaction between IL-33 and ST2 is an attractive target to control the inflammatory disease at the upstream of the signaling. However, discovering the chemical moieties that bind to the protein–protein interaction interface is a challenging task due to the relatively wide and shallow binding pocket compared to the enzyme’s active site. For the IL-33-specific binder discovery, a series of IL-33 mutants were designed, and an electrophile chemical library was screened. Herein, we described the backbone 1H, 15N, and 13C resonance assignments of three IL-33 (117–270) mutants. Based on the assignments, the binding site of a selected compound by this approach was determined by 2D NMR. These results provide valuable information for further studies in drug discovery targeting the IL-33 and ST2 interaction.

PDZ interaction of the GABA transporter GAT1 with the syntenin-1 in Neuro-2a cells

The GABA transporter GAT1 regulates brain inhibitory neurotransmission and it is considered a potential therapeutic target for the treatment of wide spectrum of neurological diseases including epilepsy, stroke and autism. Syntenin-1 binds to syntaxin 1A, which is known to regulate the plasma membrane insertion of several neurotransmitter transporters. Previously, a direct interaction of syntenin-1 with the glycine transporter GlyT2 was reported. Here, we show that the GABA transporter GAT1 also directly interacts with syntenin-1, involving both unidentified protein interaction interface and the GAT1 C-terminal PDZ binding motif interacting mainly with syntenin-1 PDZ domain 1. The PDZ interaction was eliminated by the mutation of GAT1 isoleucine 599 and tyrosine 598 located in PDZ positions 0 and -1, respectively. This indicates an unconventional PDZ interaction and possible regulation of the transporter PDZ motif via tyrosine phosphorylation. Whole syntenin-1 protein fused to GST protein and immobilised on glutathione resin coprecipitated intact GAT1 transporter from an extract of GAT1 transfected neuroblastoma N2a cells. This coprecipitation was inhibited by tyrosine phosphatases inhibitor pervanadate. The fluorescence tagged GAT1 and syntenin-1 colocalized upon coexpression in N2a cells. The above results show that syntenin-1 might be, in addition to GlyT2, directly involved in the trafficking of GAT1 transporter.

Mutated axon guidance gene PLXNB2 sustains growth and invasiveness of stem cells isolated from cancers of unknown primary.

The genetic changes sustaining the development of cancers of unknown primary (CUP) remain elusive. The whole-exome genomic profiling of 14 rigorously selected CUP samples did not reveal specific recurring mutation in known driver genes. However, by comparing the mutational landscape of CUPs with that of most other human tumor types, it emerged a consistent enrichment of changes in genes belonging to the axon guidance KEGG pathway. In particular, G842C mutation of PlexinB2 (PlxnB2) was predicted to be activating. Indeed, knocking down the mutated, but not the wild-type, PlxnB2 in CUP stem cells resulted in the impairment of self-renewal and proliferation in culture, as well as tumorigenic capacity in mice. Conversely, the genetic transfer of G842C-PlxnB2 was sufficient to promote CUP stem cell proliferation and tumorigenesis in mice. Notably, G842C-PlxnB2 expression in CUP cells was associated with basal EGFR phosphorylation, and EGFR blockade impaired the viability of CUP cells reliant on the mutated receptor. Moreover, the mutated PlxnB2 elicited CUP cell invasiveness, blocked by EGFR inhibitor treatment. In sum, we found that a novel activating mutation of the axon guidance gene PLXNB2 sustains proliferative autonomy and confers invasive properties to stem cells isolated from cancers of unknown primary, in EGFR-dependent manner.

CRISPR Screen Reveals BRD2/4 Molecular Glue-like Degrader via Recruitment of DCAF16.

Molecular glues (MGs) are monovalent small molecules that induce an interaction between proteins (native or non-native partners) by altering the protein-protein interaction (PPI) interface toward a higher-affinity state. Enhancing the PPI between a protein and E3 ubiquitin ligase can lead to degradation of the partnering protein. Over the past decade, retrospective studies of clinical drugs identified that immunomodulatory drugs (e.g., thalidomide and analogues) and indisulam exhibit a molecular glue effect by driving the interaction between non-native substrates to CRBN and DCAF15 ligases, respectively. Ensuing reports of phenotypic screens focused on MG discovery have suggested that these molecules may be more common than initially anticipated. However, prospective discovery of MGs remains challenging. Thus, expanding the repertoire of MGs will enhance our understanding of principles for prospective design. Herein, we report the results of a CRISPR/Cas9 knockout screen of over 1000 ligases and ubiquitin proteasome system components in a BRD4 degradation assay with a JQ1-based monovalent degrader, compound 1a. We identified DCAF16, a substrate recognition component of the Cul4 ligase complex, as essential for compound activity, and we demonstrate that compound 1a drives the interaction between DCAF16 and BRD2/4 to promote target degradation. Taken together, our data suggest that compound 1a functions as an MG degrader between BRD2/4 and DCAF16 and provides a foundation for further mechanistic dissection to advance prospective MG discovery.

Discovery of reactive peptide inhibitors of human papillomavirus oncoprotein E6.

Human papillomavirus (HPV) infections account for nearly all cervical cancer cases, which is the fourth most common cancer in women worldwide. High-risk variants, including HPV16, drive tumorigenesis in part by promoting the degradation of the tumor suppressor p53. This degradation is mediated by the HPV early protein 6 (E6), which recruits the E3 ubiquitin ligase E6AP and redirects its activity towards ubiquitinating p53. Targeting the protein interaction interface between HPV E6 and E6AP is a promising modality to mitigate HPV-mediated degradation of p53. In this study, we designed a covalent peptide inhibitor, termed reactide, that mimics the E6AP LXXLL binding motif by selectively targeting cysteine 58 in HPV16 E6 with quantitative conversion. This reactide provides a starting point in the development of covalent peptidomimetic inhibitors for intervention against HPV-driven cancers.

Structural systems biology approach delineate the functional implications of SNPs in exon junction complex interaction network

In eukaryotes, transcripts that carry premature termination codons (PTC) leading to truncated proteins are degraded by the Nonsense Mediated Decay (NMD) machinery. Missense and nonsense Single Nucleotide Polymorphisms (SNPs) in proteins belonging to Exon junction complex (EJC) and up-frameshift protein (UPF) will compromise NMD leading to the accumulation of truncated proteins in various diseases. The EJC and UPF which are involved in NMD is a good model system to study the effect of SNPs at a system level. Despite the availability of crystal structures, computational tools, and data on mutational and deletion studies, with functional implications, an integrated effort to understand the impact of SNPs at the systems level is lacking. To study the functional consequences of missense SNPs, sequence-based techniques like SIFT and PolyPhen which classify SNPs as deleterious or non-deleterious and structure-based methods like FoldX which calculate the Delta Delta G, (ddGs, ∆∆G) are used. Using FoldX, the ddG for mutations with experimentally validated functional effects is calculated and compared with those calculated for SNPs in the same protein–protein interaction interface. Further, a model is conceived to explain the functional implications of SNPs based on the effects observed for known mutants. The results are visualized in a network format. The effects of nonsense mutations are discerned by comparing with deletion mutation studies and loss of interaction in the crystal structure. The present work not only integrates genomics, proteomics, and classical genetics with ‘Structural Biology’ but also helps to integrate it into a ‘systems-level functional network’. Communicated by Ramaswamy H. Sarma

IRAA: A statistical tool for investigating a protein-protein interaction interface from multiple structures.

Understanding protein-protein interactions (PPIs) is fundamental to infer how different molecular systems work. A major component to model molecular recognition is the buried surface area (BSA), that is, the area that becomes inaccessible to solvent upon complex formation. To date, many attempts tried to connect BSA to molecular recognition principles, and in particular, to the underlying binding affinity. However, the most popular approach to calculate BSA is to use a single (or in some cases few) bound structures, consequently neglecting a wealth of structural information of the interacting proteins derived from ensembles corresponding to their unbound and bound states. Moreover, the most popular method inherently assumes the component proteins to bind as rigid entities. To address the above shortcomings, we developed a Monte Carlo method-based Interface Residue Assessment Algorithm (IRAA), to calculate a combined distribution of BSA for a given complex. Further, we apply our algorithm to human ACE2 and SARS-CoV-2 Spike protein complex, a system of prime importance. Results show a much broader distribution of BSA compared to that obtained from only the bound structure or structures and extended residue members of the interface with implications to the underlying biomolecular recognition. We derive that specific interface residues of ACE2 and of S-protein are consistently highly flexible, whereas other residues systematically show minor conformational variations. In effect, IRAA facilitates the use of all available structural data for any biomolecular complex of interest, extracting quantitative parameters with statistical significance, thereby providing a deeper biophysical understanding of the molecular system under investigation.

ProteinGLUE multi-task benchmark suite for self-supervised protein modeling.

Self-supervised language modeling is a rapidly developing approach for the analysis of protein sequence data. However, work in this area is heterogeneous and diverse, making comparison of models and methods difficult. Moreover, models are often evaluated only on one or two downstream tasks, making it unclear whether the models capture generally useful properties. We introduce the ProteinGLUE benchmark for the evaluation of protein representations: a set of seven per-amino-acid tasks for evaluating learned protein representations. We also offer reference code, and we provide two baseline models with hyperparameters specifically trained for these benchmarks. Pre-training was done on two tasks, masked symbol prediction and next sentence prediction. We show that pre-training yields higher performance on a variety of downstream tasks such as secondary structure and protein interaction interface prediction, compared to no pre-training. However, the larger base model does not outperform the smaller medium model. We expect the ProteinGLUE benchmark dataset introduced here, together with the two baseline pre-trained models and their performance evaluations, to be of great value to the field of protein sequence-based property prediction. Availability: code and datasets from https://github.com/ibivu/protein-glue.

Structure of the DNMT3B ADD domain suggests the absence of a DNMT3A-like autoinhibitory mechanism

De novo DNA methylation in early mammalian development depends on the activity of the DNMT3 methyltransferase family. An autoinhibitory mechanism involving the interaction between ADD and the catalytic domains of DNMT3A has been described. ADD is a zinc-coordinating histone-binding domain. The ADD domain of DNMT3A, when bound to a K4-unmethylated histone H3 tail, switches the enzyme to its catalytically active state. DNMT3B is another de novo methyltransferase enzyme with a more strict tissue- and stage-specific expression profile and a slightly different site specificity, lacking cooperative DNA methylation activity. Here, we obtained the crystal structure of the DNMT3B ADD domain, which demonstrated the extended conformation of the autoinhibitory loop even in the absence of the histone H3 tail. The lack of interaction between DNMT3B ADD and the methyltransferase domain was confirmed using an in vitro pull-down assay. The structural rearrangements in the loop also created an additional protein interaction interface leading to the formation of trimers in crystal, which may reflect their possible involvement in some unknown protein–protein interactions. Our results suggest that DNMT3B, in contrast to DNMT3A, has different modes of regulation of its activity that are independent of H3K4 methylation status.

Interface investigation the protein interaction interface between malate dehydrogenase and citrate synthase

Enzymes complexes (metabolons) support the metabolic reactions through substrate channeling. Several enzymes in the Krebs cycle are found in protein‐protein metabolons including mitochondrial malate dehydrogenase (MDH2) and citric synthase (CS). While the weak transitory interaction between these enzymes has been demonstrated, the key residues of human MDH2 involved in the interface with CS has not been identified. The purpose of this study is to probe possible residues of MDH responsible for binding to CS. Because cytosolic MDH (MDH1) poorly binds to CS, we initially identified unique sequences that were involved in or adjacent to reported or predicted binding sites of MDH2 and CS. Four regions were selected and residues corresponding to the primary sequence of cytosolic MDH1 were substituted in place of mitochondrial MDH2. Computational models of all the monomeric constructs were made using phyre2 homology modeling, subsequent refinement of the monomers using Galaxy Refine and construction of the dimer forms using the appropriate template in PyMol. Dimers were then refined using Galaxy Refine Complex to give replicate structures of the final models. Potential metabolon formation was explored using HawkDock with the dimer versions of MDH and Citrate Synthase either with or without restraints imposed by published metabolon models.Wild‐type human genes of MDH1, MDH2, and CS were codon optimized for bacterial expression and cloned into the C‐terminus of each gene in a pET28a expression vector. Each MDH1‐MDH2 substitution (DS1‐DS4) were constructed by Gibson cloning. Resulting structure and functional impacts were determined by melting points and kinetic parameters (specific activity, Km, Vmax and Kcat) in purified proteins. Interactions between wild‐type MDH1/2 and CS were compared to MDH2 DS mutants. To qualitatively demonstrate interactions, pull‐down assays were performed in the absence and presence of crowding agents, glycerol, PEG or Ficoll 70. Finally wild‐type MDH 1 and 2 vs MDH2 mutants were subjected to competitive pull‐down assays to identify regions responsible for isozyme specific interactions. This work will show four of the potential interacting regions between MDH and CS and lead to a better understanding of dynamics of this metabolic pair.

WWC proteins mediate LATS1/2 activation by Hippo kinases and imply a tumor suppression strategy

YAP and TAZ (YAP/TAZ), two major effectors of the Hippo signaling pathway, are frequently activated in human cancers. The activity of YAP/TAZ is strictly repressed upon phosphorylation by LATS1/2 tumor suppressors. However, it is unclear how LATS1/2 are precisely regulated by upstream factors such as Hippo kinases MST1/2. Here, we show that WWC proteins (WWC1/2/3) directly interact with LATS1/2 and SAV1, and SAV1, in turn, brings in MST1/2 to phosphorylate and activate LATS1/2. Hence, WWC1/2/3 play an organizer role in a signaling module that mediates LATS1/2 activation by MST1/2. Moreover, we have defined a minimum protein interaction interface on WWC1/2/3 that is sufficient to activate LATS1/2 in a robust and specific manner. The corresponding minigene, dubbed as SuperHippo, can effectively suppress tumorigenesis in multiple tumor models. Our study has uncovered a molecular mechanism underlying LATS1/2 regulation and provides a strategy for treating diverse malignancies related to Hippo pathway dysregulation.

A fusion of the Bacteroides fragilis ferrous iron import proteins reveals a role for FeoA in stabilizing GTP-bound FeoB

Iron is an essential element for nearly all organisms, and under anoxic and/or reducing conditions, Fe2+ is the dominant form of iron available to bacteria. The ferrous iron transport (Feo) system is the primary prokaryotic Fe2+ import machinery, and two constituent proteins (FeoA and FeoB) are conserved across most bacterial species. However, how FeoA and FeoB function relative to one another remains enigmatic. In this work, we explored the distribution of feoAB operons encoding a fusion of FeoA tethered to the N-terminal, G-protein domain of FeoB via a connecting linker region. We hypothesized that this fusion poises FeoA to interact with FeoB to affect function. To test this hypothesis, we characterized the soluble NFeoAB fusion protein from Bacteroides fragilis, a commensal organism implicated in drug-resistant infections. Using X-ray crystallography, we determined the 1.50-Å resolution structure of BfFeoA, which adopts an SH3-like fold implicated in protein–protein interactions. Using a combination of structural modeling, small-angle X-ray scattering, and hydrogen–deuterium exchange mass spectrometry, we show that FeoA and NFeoB interact in a nucleotide-dependent manner, and we mapped the protein–protein interaction interface. Finally, using guanosine triphosphate (GTP) hydrolysis assays, we demonstrate that BfNFeoAB exhibits one of the slowest known rates of Feo-mediated GTP hydrolysis that is not potassium-stimulated. Importantly, truncation of FeoA from this fusion demonstrates that FeoA–NFeoB interactions function to stabilize the GTP-bound form of FeoB. Taken together, our work reveals a role for FeoA function in the fused FeoAB system and suggests a function for FeoA among prokaryotes.

PCNA in Cervical Intraepithelial Neoplasia and Cervical Cancer: An Interaction Network Analysis of Differentially Expressed Genes

The investigation of differentially expressed genes (DEGs) and their interactome could provide valuable insights for the development of markers to optimize cervical intraepithelial neoplasia (CIN) screening and treatment. This study investigated patients with cervical disease to identify gene markers whose dysregulated expression and protein interaction interface were linked with CIN and cervical cancer (CC). Literature search of microarray datasets containing cervical epithelial samples was conducted in Gene Expression Omnibus and Pubmed/Medline from inception until March 2021. Retrieved DEGs were used to construct two protein-protein interaction (PPI) networks. Module DEGs that overlapped between CIN and CC samples, were ranked based on 11 topological algorithms. The highest-ranked hub gene was retrieved and its correlation with prognosis, tissue expression and tumor purity in patients with CC, was evaluated. Screening of the literature yielded 9 microarray datasets (GSE7803, GSE27678, GSE63514, GSE6791, GSE9750, GSE29570, GSE39001, GSE63678, GSE67522). Two PPI networks from CIN and CC samples were constructed and consisted of 1704 and 3748 DEGs along 21393 and 79828 interactions, respectively. Two gene clusters were retrieved in the CIN network and three in the CC network. Multi-algorithmic topological analysis revealed PCNA as the highest ranked hub gene between the two networks, both in terms of expression and interactions. Further analysis revealed that while PCNA was overexpressed in CC tissues, it was correlated with favorable prognosis (log-rank P=0.022, HR=0.58) and tumor purity (P=9.86 × 10-4, partial rho=0.197) in CC patients. This study identified that cervical PCNA exhibited multi-algorithmic topological significance among DEGs from CIN and CC samples. Overall, PCNA may serve as a potential gene marker of CIN progression. Experimental validation is necessary to examine its value in patients with cervical disease.

Second-generation DNA-encoded multiple display on a constant macrocyclic scaffold enabled by an orthogonal protecting group strategy

DNA-encoded chemical library (DEL) represents an emerging drug discovery technology to construct compound libraries with abundant chemical combinations. While drug-like small molecule DELs facilitate the discovery of binders against targets with defined pockets, macrocyclic DELs harboring extended scaffolds enable targeting of the protein–protein interaction (PPI) interface. We previously demonstrated the design of the first-generation DNA-encoded multiple display based on a constant macrocyclic scaffold, which harvested binders against difficult targets such as tumor necrosis factor-α (TNF-α). Here, we developed a novel strategy which utilized four orthogonal amine-protecting groups on DNA, to explore larger chemical combinations on the same constant macrocyclic scaffold, following the parallel paradigm to mimic the versatile antibody-like multivalent epitope recognition patterns. We successfully integrated these orthogonal protecting groups with acylation and made a mock second-generation DNA-encoded display combination. This work illustrates a strategy to produce larger encoded multiple display on a constant macrocyclic scaffold, which could facilitate potential binder discovery with enhanced affinity to clinically significant PPI targets.

Development of Murine Leukemia Virus Integrase-Derived Peptides That Bind Brd4 Extra-Terminal Domain as Candidates for Suppression of Acute Myeloid Leukemia.

The bromodomain and extra-terminal (BET) domain family of proteins, which include its prototypical member Brd4, is implicated in a variety of cancers and viral infections due to their interaction with cellular and viral proteins. BET proteins contain two bromodomains, a common protein motif that selectively binds acetylated lysine on histones. However, they are structurally distinct from other bromodomain-containing proteins because they encode a unique C-terminal extra-terminal (ET) domain that is important for the protein-protein interactions including jumonji C-domain-containing protein 6 (JMJD6) and histone-lysine N-methyltransferase NSD3 (NSD3). Brd4 functions primarily during transcription as a passive scaffold linking cellular and viral proteins to chromatin. The rapid development of clinical inhibitors targeting Brd4 highlights the importance of this protein as an anticancer target. Current therapeutic approaches focus on the development of small molecule acetylated lysine mimics of histone marks that block the ability of the bromodomains to bind their chromatin marks. Thus far, bromodomain-targeted agents have shown dose-limiting toxicities due to off-target effects on other bromodomain-containing proteins. Here, we exploited a viral-host protein interaction interface to design peptides for the disruption of BET protein function. A murine leukemia virus (MLV) integrase-derived peptide (ET binding motif, EBM) and its shorter minimal binding motif (pentapeptide LKIRL) were sufficient to directly bind the Brd4 ET domain and reduce cellular proliferation of an acute myeloid leukemia cell line. Using computational and biochemical approaches, we identified the minimal essential contacts between EBM and LKIRL peptides and the Brd4 ET domain. Our findings provide a structural foundation for inhibiting BET/Brd4-mediated cancers by targeting the ET domain with small peptide-based inhibitors.